RAIM and AUKF for GNSS Performance Enhancement in Multi-Constellation

Meng, Fanchen; Meng, Xiangxu; Liu, Tao; Sun, Jing; Niu, Zun; Zhu, Bing

doi:10.33012/2017.14932

Cited by 1 publication

(2 citation statements)

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“…In GNSSs, there are some existing enhancement methods that have the ability to improve the reliability of positioning services. For example, Meng et al [37], [38] pointed out that the positioning reliability in GNSS could be improved by appropriately selecting satellites from multiple constellations. For the well-known ground-based augmentation system (GBAS), the reliability of GNSS positioning is enhanced by utilizing a large amount of ground stations to monitor GNSS signals and broadcast the monitoring information to users [39], [40].…”

Section: B Related Workmentioning

confidence: 99%

“…Thus, it is reasonable for the minimum detectable error to decrease at the beginning of the increase of the tolerable FA rate. In addition, as the tolerable FA rate further increases, some observation events that originally need to be considered are now ignored according to equation (37). The occurrence of this situation may change the sample point corresponding to the minimum detectable error, resulting in fluctuations in service reliability.…”

Section: Key Factors Affecting the Service Reliabilitymentioning

confidence: 99%

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Toward Reliable UAV-Enabled Positioning in Mountainous Environments: System Design and Preliminary Results

Wang

Liu

et al. 2022

IEEE Trans. Rel.

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Reliable positioning services are extremely important for users in mountainous environments. However, in such environments, the service reliability of conventional wireless positioning technologies is often disappointing due to frequent nonline-of-sight (NLoS) propagation and poor geometry of available anchor nodes. Hence, we propose a unmanned aerial vehicle (UAV)-enabled positioning system that utilizes UAV's mobility to overcome the above challenges. In this article, we first analyze and model the major causes of service failures in the proposed system. In particular, a geometry-based NLoS probability model is established based on the digital elevation models (DEM) of realistic terrain for reliability analysis. Subsequently, we propose a reliability prediction method and derive the corresponding metric to evaluate the system's ability to provide reliable positioning services. Moreover, we also develop a voting-based method for the further enhancement of service reliability. Monte-Carlo simulations show that in mountainous environments, the proposed reliability prediction method could achieve a prediction accuracy that is at least 36.8% higher than that of the existing technique. In addition, in the experiments conducted in two typical valley scenarios, the proposed reliability enhancement method improves the service reliability of the proposed system by 23% and 29%, respectively. These numerical results demonstrate the strong potential of the proposed system and methods for reliable positioning.

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Section: B Related Workmentioning

confidence: 99%

Section: Key Factors Affecting the Service Reliabilitymentioning

confidence: 99%